Missing data is a challenging issue that needs to be addressed throughout the planning, implementation, and analysis phases of any investigation. However, approaches for missing data are particularly important when conducting longitudinal intervention studies. This presentation will highlight strategies to manage missing data, including: (a) preventing missing data; (b) retrieving missing data; (c) understanding the patterns of missing data; (d) determining whether the data are missing at random; (e) considering the amount of missing data and the impact on data analyses; and (f) data analytic strategies for addressing missing data. Prevention of missing data begins with careful planning of the study and piloting of the intervention protocol. For example, pilot studies allow for identifying issues associated with subject burden or fatigue, clarity of instruments or questions, sensitivity or language of the questions, and data collection mechanisms (e.g., mail, telephone, face-to-face). Anticipating and addressing the potential problem of missing data prior to implementation of a full-scale study is critical. During implementation of the study, critical strategies to monitor and minimize missing data will be addressed. Strategies to determine whether missing data are retrievable will be discussed including: reviewing the original data, re-interviewing respondents, or reviewing selected archival data. Reasons for missing data (e.g., whether data are missing at random or not at random), and the amount of missing data have implications for data analyses. Selected data analytic strategies will be highlighted.